基于KKT和量子遗传算法的风火电联合上网最优决策  

Optimal Decision for Joint Wind-thermal Power Based on KKT and Quantum Genetic Algorithm

作　　者：魏亚楠[1] 牛东晓[1] 

机构地区：[1]华北电力大学经济与管理学院,北京102206

出　　处：《运筹与管理》2013年第2期105-110,共6页Operations Research and Management Science

基　　金：国家自然科学基金资助项目(71071052);中央高校基本科研业务费专项资金资助(12QX22)

摘　　要：研究了同时考虑节能减排效益和经济效益时,风火电联合上网的决策模型,并采用提出的KKT框架下的量子遗传算法进行模型的求解。综合考虑风电和火电的特点,建立经济效益函数和节能减排效益函数以及相关的约束条件,最终确立多目标决策模型。在KKT框架下将多目标函数转化为单目标,并利用量子遗传算法进行模型的求解。算例分析显示本文提出的KKT框架下的量子遗传算法在决策模型的求解时能够利用更少的CPU运行时间获得更优的决策结果,与其他常用的优化模型相比具有较高的优越性。The decision-making model for joint wind-thermal power considering both economic benefit and energy conservation and emission reduction benefit is investigated by KKT and solved by quantum genetic algorithm. Considering the characteristics of wind power and thermal power, economic benefit function and energy conserva- tion and emission reduction function are established as well as relevant constraints, and finally a multi-objective decision-making model is established. The multi-objective model is changed into single goal in KKT framework and solved by quantum genetic algorithm. A numerical example is given which suggests that the proposed KKT and quantum genetic algorithm is more accurate and with less computational time than commonly used optimiza- tion methods. It has great superiority in decision-making for ioint wind-thermal power.

关 键 词：风火电力系统 决策模型 KKT 量子遗传算法 节能减排 

分 类 号：TM73[电气工程—电力系统及自动化]